Medical image classification method, model training method, computing device, and storage medium

Abstract

This application describes a medical image classification method, a model training method, and a server. The medical image classification method includes: obtaining, by a device, a medical image data set. The device includes a memory storing instructions and a processor in communication with the memory. The method includes performing, by the device, quality analysis on the medical image data set, to extract feature information of a medical image in the medical image data set; and classifying, by the device, the medical image data set based on the feature information and by using a pre-trained deep learning network for performing anomaly detection and classification, to obtain a classification result.

What is claimed is: 
     
       1. A method for classifying a medical image, the method comprising:
 obtaining, by a device comprising a memory storing instructions and a processor in communication with the memory, a medical image data set; 
 performing, by the device, quality analysis on the medical image data set, to extract feature information of a medical image in the medical image data set, the quality analysis comprising at least one of the following: hue-saturation-lightness analysis, definition analysis, texture analysis, or entropy value analysis, by:
 performing, by the device, at least one of the following: the hue-saturation-lightness analysis, the definition analysis, the texture analysis, or the entropy value analysis on the medical image data set, to extract the feature information of the medical image, wherein the feature information comprises at least one of the following: hue feature information, saturation feature information, lightness feature information, a definition index, a grayscale edge, or an entropy value; and 
 
 classifying, by the device, the medical image data set based on the feature information and by using a pre-trained deep learning network for performing anomaly detection and classification, to obtain a classification result, 
 wherein:
 performing the hue-saturation-lightness analysis comprises converting values of a red coordinate, a green coordinate, and a blue coordinate of the medical image from an RGB space to an HSV space, to obtain hue feature information, saturation feature information, and lightness feature information; 
 performing the definition analysis comprises:
 calculating a value of a digital image matrix of the medical image, 
 performing convolution on the value of the digital image matrix and a 5×5 Gaussian convolution kernel to obtain a convolution value, and 
 calculating a definition index of the medical image based on a minimum mean square error of the value of the digital image matrix and the convolution value; 
 
 performing the texture analysis comprises extracting a grayscale edge of the medical image by using a Sobel verification operator; and 
 performing the entropy value analysis comprises calculating an entropy value according to a length of the medical image, a width of the medical image, and a quantity of times and a probability that a grayscale value of a center pixel in a sliding window and an average of grayscale values other than that of the center pixel in the sliding window occur in the medical image. 
 
 
     
     
       2. The method according to  claim 1 , wherein the classifying the medical image data set based on the feature information and by using the pre-trained deep learning network for performing anomaly detection and classification comprises:
 removing, by the device, an irrelevant image from the medical image data set according to the feature information, wherein the irrelevant image comprises a non-medical image; and 
 classifying, by the device using the pre-trained deep learning network for performing anomaly detection and classification, the medical image data set from which the irrelevant image is removed. 
 
     
     
       3. The method according to  claim 1 , further comprising:
 before performing the quality analysis:
 performing, by the device, file check on the medical image data set to determine whether the medical image in the medical image data set is capable of being parsed; and 
 in response to determining that whether the medical image in the medical image data set is capable of being parsed, decoding, by the device, the medical image data set to convert the medical image into a digital image matrix. 
 
 
     
     
       4. The method according to  claim 1 , further comprising:
 before classifying the medical image data set, performing, by the device, interpolation and normalization on the medical image in the medical image data set, wherein the interpolation comprises bilinear interpolation. 
 
     
     
       5. The method according to  claim 1 , further comprising:
 training, by the device, the pre-trained deep learning network by selecting an Inception V3 model. 
 
     
     
       6. An apparatus for classifying a medical image, the apparatus comprising:
 a memory storing instructions; and 
 a processor in communication with the memory, wherein, when the processor executes the instructions, the processor is configured to cause the apparatus to:
 obtain a medical image data set, 
 perform quality analysis on the medical image data set, to extract feature information of a medical image in the medical image data set, the quality analysis comprises at least one of the following: hue-saturation-lightness analysis, definition analysis, texture analysis, or entropy value analysis, by:
 performing at least one of the following: the hue-saturation-lightness analysis, the definition analysis, the texture analysis, or the entropy value analysis on the medical image data set, to extract the feature information of the medical image, wherein the feature information comprises at least one of the following: hue feature information, saturation feature information, lightness feature information, a definition index, a grayscale edge, or an entropy value, and 
 
 classify the medical image data set based on the feature information and by using a pre-trained deep learning network for performing anomaly detection and classification, to obtain a classification result, 
 
 wherein:
 when the processor is configured to cause the apparatus to perform the hue-saturation-lightness analysis, the processor is configured to cause the apparatus to convert values of a red coordinate, a green coordinate, and a blue coordinate of the medical image from an RGB space to an HSV space, to obtain hue feature information, saturation feature information, and lightness feature information; 
 when the processor is configured to cause the apparatus to perform the definition analysis, the processor is configured to cause the apparatus to:
 calculate a value of a digital image matrix of the medical image, 
 perform convolution on the value of the digital image matrix and a 5×5 Gaussian convolution kernel to obtain a convolution value, and 
 calculate a definition index of the medical image based on a minimum mean square error of the value of the digital image matrix and the convolution value; 
 
 when the processor is configured to cause the apparatus to perform the texture analysis, the processor is configured to cause the apparatus to extract a grayscale edge of the medical image by using a Sobel verification operator; and 
 when the processor is configured to cause the apparatus to perform the entropy value analysis, the processor is configured to cause the apparatus to calculate an entropy value according to a length of the medical image, a width of the medical image, and a quantity of times and a probability that a grayscale value of a center pixel in a sliding window and an average of grayscale values other than that of the center pixel in the sliding window occur in the medical image. 
 
 
     
     
       7. The apparatus according to  claim 6 , wherein, when the processor is configured to cause the apparatus to classify the medical image data set based on the feature information and by using the pre-trained deep learning network for performing anomaly detection and classification, the processor is configured to cause the apparatus to:
 remove an irrelevant image from the medical image data set according to the feature information, wherein the irrelevant image comprises a non-medical image; and 
 classify, by using the pre-trained deep learning network for performing anomaly detection and classification, the medical image data set from which the irrelevant image is removed. 
 
     
     
       8. The apparatus according to  claim 6 , wherein, before the processor is configured to cause the apparatus to perform the quality analysis, the processor is configured to further cause the apparatus to:
 perform file check on the medical image data set to determine whether the medical image in the medical image data set is capable of being parsed; and 
 in response to determining that whether the medical image in the medical image data set is capable of being parsed, decode the medical image data set to convert the medical image into a digital image matrix. 
 
     
     
       9. The apparatus according to  claim 6 , wherein, before the processor is configured to cause the apparatus to classify the medical image data set, the processor is configured to further cause the apparatus to:
 perform interpolation and normalization on the medical image in the medical image data set, wherein the interpolation comprises bilinear interpolation. 
 
     
     
       10. The apparatus according to  claim 6 , wherein, when the processor executes the instructions, the processor is configured to further cause the apparatus to:
 train the pre-trained deep learning network by selecting an Inception V3 model. 
 
     
     
       11. A non-transitory computer-readable storage medium, storing computer-readable instructions, wherein, the computer-readable instructions, when executed by a processor, are configured to cause the processor to perform:
 obtaining a medical image data set; 
 performing quality analysis on the medical image data set, to extract feature information of a medical image in the medical image data set, the quality analysis comprising at least one of the following: hue-saturation-lightness analysis, definition analysis, texture analysis, or entropy value analysis, by:
 performing at least one of the following: the hue-saturation-lightness analysis, the definition analysis, the texture analysis, or the entropy value analysis on the medical image data set, to extract the feature information of the medical image, wherein the feature information comprises at least one of the following: hue feature information, saturation feature information, lightness feature information, a definition index, a grayscale edge, or an entropy value; and 
 
 classifying the medical image data set based on the feature information and by using a pre-trained deep learning network for performing anomaly detection and classification, to obtain a classification result, 
 wherein:
 when the computer-readable instructions are configured to cause the processor to perform performing the hue-saturation-lightness analysis, the computer-readable instructions are configured to cause the processor to perform converting values of a red coordinate, a green coordinate, and a blue coordinate of the medical image from an RGB space to an HSV space, to obtain hue feature information, saturation feature information, and lightness feature information; 
 when the computer-readable instructions are configured to cause the processor to perform performing the definition analysis, the computer-readable instructions are configured to cause the processor to perform:
 calculating a value of a digital image matrix of the medical image, 
 performing convolution on the value of the digital image matrix and a 5×5 Gaussian convolution kernel to obtain a convolution value, and 
 calculating a definition index of the medical image based on a minimum mean square error of the value of the digital image matrix and the convolution value; 
 
 when the computer-readable instructions are configured to cause the processor to perform performing the texture analysis, the computer-readable instructions are configured to cause the processor to perform extracting a grayscale edge of the medical image by using a Sobel verification operator; and 
 when the computer-readable instructions are configured to cause the processor to perform performing the entropy value analysis, the computer-readable instructions are configured to cause the processor to perform calculating an entropy value according to a length of the medical image, a width of the medical image, and a quantity of times and a probability that a grayscale value of a center pixel in a sliding window and an average of grayscale values other than that of the center pixel in the sliding window occur in the medical image. 
 
 
     
     
       12. The non-transitory computer-readable storage medium according to  claim 11 , wherein, when the computer-readable instructions are configured to cause the processor to perform classifying the medical image data set based on the feature information and by using the pre-trained deep learning network for performing anomaly detection and classification, the computer-readable instructions are configured to cause the processor to perform:
 removing an irrelevant image from the medical image data set according to the feature information, wherein the irrelevant image comprises a non-medical image; and 
 classifying, by using the pre-trained deep learning network for performing anomaly detection and classification, the medical image data set from which the irrelevant image is removed. 
 
     
     
       13. The non-transitory computer-readable storage medium according to  claim 11 , wherein, before the computer-readable instructions are configured to cause the processor to perform performing the quality analysis, the computer-readable instructions are configured to further cause the processor to perform:
 performing file check on the medical image data set to determine whether the medical image in the medical image data set is capable of being parsed; and 
 in response to determining that whether the medical image in the medical image data set is capable of being parsed, decoding the medical image data set to convert the medical image into a digital image matrix. 
 
     
     
       14. The non-transitory computer-readable storage medium according to  claim 11 , wherein, before the computer-readable instructions are configured to cause the processor to perform classifying the medical image data set, the computer-readable instructions are configured to further cause the processor to perform:
 performing interpolation and normalization on the medical image in the medical image data set, wherein the interpolation comprises bilinear interpolation.